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A priori data-driven multi-clustered reservoir generation algorithm for echo state network
Echo state networks (ESNs) with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the res...
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Published in: | PloS one 2015-04, Vol.10 (4), p.e0120750-e0120750 |
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description | Echo state networks (ESNs) with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision. |
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However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0120750</identifier><identifier>PMID: 25875296</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Analysis ; Cluster Analysis ; Clustering ; Complexity ; Computer Simulation ; Database Management Systems ; Databases, Factual ; Entropy (Information theory) ; Evaluation ; Forecasts and trends ; Models, Neurological ; Neural Networks (Computer) ; Reservoirs ; Reservoirs (Water) ; Topology ; Windows (intervals)</subject><ispartof>PloS one, 2015-04, Vol.10 (4), p.e0120750-e0120750</ispartof><rights>COPYRIGHT 2015 Public Library of Science</rights><rights>2015 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Li et al 2015 Li et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-3602ca941b8a1f2a4ac3b639e081edf848ad56d89a2221c3cfe5de3da7dd23fe3</citedby><cites>FETCH-LOGICAL-c692t-3602ca941b8a1f2a4ac3b639e081edf848ad56d89a2221c3cfe5de3da7dd23fe3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1673120821/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1673120821?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25875296$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Gao, Zhong-Ke</contributor><creatorcontrib>Li, Xiumin</creatorcontrib><creatorcontrib>Zhong, Ling</creatorcontrib><creatorcontrib>Xue, Fangzheng</creatorcontrib><creatorcontrib>Zhang, Anguo</creatorcontrib><title>A priori data-driven multi-clustered reservoir generation algorithm for echo state network</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Echo state networks (ESNs) with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Cluster Analysis</subject><subject>Clustering</subject><subject>Complexity</subject><subject>Computer Simulation</subject><subject>Database Management Systems</subject><subject>Databases, Factual</subject><subject>Entropy (Information theory)</subject><subject>Evaluation</subject><subject>Forecasts and trends</subject><subject>Models, Neurological</subject><subject>Neural Networks (Computer)</subject><subject>Reservoirs</subject><subject>Reservoirs (Water)</subject><subject>Topology</subject><subject>Windows 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Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Xiumin</au><au>Zhong, Ling</au><au>Xue, Fangzheng</au><au>Zhang, Anguo</au><au>Gao, Zhong-Ke</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A priori data-driven multi-clustered reservoir generation algorithm for echo state network</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2015-04-13</date><risdate>2015</risdate><volume>10</volume><issue>4</issue><spage>e0120750</spage><epage>e0120750</epage><pages>e0120750-e0120750</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Echo state networks (ESNs) with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>25875296</pmid><doi>10.1371/journal.pone.0120750</doi><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Analysis Cluster Analysis Clustering Complexity Computer Simulation Database Management Systems Databases, Factual Entropy (Information theory) Evaluation Forecasts and trends Models, Neurological Neural Networks (Computer) Reservoirs Reservoirs (Water) Topology Windows (intervals) |
title | A priori data-driven multi-clustered reservoir generation algorithm for echo state network |
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